Systems and methods for production and logistics management

Abstract

Systems and methods are disclosed for providing a product to consumers by receiving the product from a manufacturer located in a first country; matching the products with a seller in the second country; managing delivery logistics by matching available third-party truckers and third-party warehouses in a second country based on proximity; forecasting demand using one or more neural networks for the manufacturer, the logistics organization, and the warehouses; and managing logistics for the manufacturer through a dashboard populated by the forecasted demand from the one or more neural networks.

What is claimed is: 
     
       1. A method for delivering a product, comprising:
 Receiving, at a manufacturer processor, a customer order for the product specified using a computer or a smart phone of the customer; 
 extracting, by the manufacturer processor, order information from the customer order comprising a geographic location of the customer and mailing address; 
 capturing, using a digital camera, a plurality of images forming a three-dimensional (3D) representation of the product; 
 training, by the manufacturer processor, using machine learning to generate a 3D model of the product with the plurality of images; 
 transmitting, from the manufacturer processor, the trained 3D model to the customer computer or smart phone; 
 executing the 3D model on the customer computer or smart phone to calibrate and measure dimensions of a customer environment; 
 scaling the 3D model to the dimensions of the customer environment at a size range adjusting by the customer; 
 rendering, by the manufacturer processor, the scaled 3D model over a view of the customer environment and allowing the customer to virtually try the product using an augmented reality display; 
 forecasting, using one or more neural networks, demand of products fulfilling the orders comprising recommending a number of products for each product type including brands, styles, and sizes according to the customer environment; 
 managing logistics for the manufacturer through a dashboard populated by the forecasted demand from the one or more neural networks; and 
 managing delivery logistics by matching available third-party truckers and third-party warehouses based on proximity. 
 
     
     
       2. The method of  claim 1 , further comprising:
 creating a codebook of deformable body models; 
 shape matching the 3D model by matching points on the body to the deformable model; 
 generating a match score with information about clothing using a product distance function with an exact match of brand, clothing type, style and size assigned a distance of zero and a match for only brand, clothing type and size is assigned a positive number and differences in size produce proportionally increased positive numbers; and 
 wherein recommending a number of products comprises one or more of: clothing, footwear, headgear, and furniture based on the match score. 
 
     
     
       3. The method of  claim 1 , further comprising: forecasting, by a third-party processor using the one or more neural networks, demand of products fulfilling the orders including recommending a number of products for each product type including brands, styles, and sizes according to the customer environment. 
     
     
       4. The system of  claim 3 , wherein the product includes clothing or furniture.